Data-driven analysis to understand Long COVID using electronic health records from the RECOVER Initiative
Zang, C; Zhang, Y; Xu, J; et al., Nature Communications
Published
April 2023
Journal
Nature Communications
Abstract
Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.
Authors
Chengxi Zang, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Edward J Schenck, Dhruv Khullar, Anna S Nordvig, Elizabeth A Shenkman, Russell L Rothman, Jason P Block, Kristin Lyman, Mark G Weiner, Thomas W Carton, Fei Wang, Rainu Kaushal
Keywords
Humans; Post-Acute COVID-19 Syndrome; COVID-19/epidemiology; Electronic Health Records; SARS-CoV-2; Propensity Score
Short Summary
RECOVER researchers analyzed electronic health records (EHR) in order to define Long COVID. Researchers found up to 25 different symptoms that patients who had COVID were more likely to have than those who didn’t have COVID. The symptoms were related to many different organs, such as memory loss, hair loss, and feeling tired. They found that certain types of Long COVID symptoms were more likely to happen in patients who were older, had more severe COVID, or had more health problems before they had COVID.
This research is important because the findings show that Long COVID affects many organs, and types of Long COVID symptoms differ between certain groups of patients. However, EHR findings are limited in that they can only look at data from the patients' past. In order to confirm these findings, future studies that follow patients' symptoms over time, into the future, are needed.